Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm
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Available online at http://jurnal.ahmar.id/index.php/asci
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021)
https://doi.org/10.35877/454RI.asci112
Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm
Kanagasabai Lenin
Department of EEE, Prasad V.Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh -520007, India
Abstract
This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog
leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of
virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every
memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed
Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in
each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two
acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm
(EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm
reduced the real power loss considerably.
© 2020 Author(s). All rights reserved.
Keywords: Optimal Reactive Power, Transmission loss, Frog leaping algorithm
1. Introduction*
Reactive power problem plays an important role in secure and economic operations of power system. Numerous types
of methods [1-6] have been utilized to solve the optimal reactive power problem. However many scientific difficulties
are found while solving problem due to an assortment of constraints. Evolutionary techniques [7-16] are applied to
solve the reactive power problem. This paper proposes an Enhanced Frog Leaping Algorithm (EFLA) to solve the
optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the
wetland and foraging deeds. F frogs are engendered at initial to structure a group, in which frog i of the group is
symbolized as then individual frogs in the group are arranged in downhill order with reference to
the fitness values, in order to discover the global most excellent solution. In the proposed Enhanced Frog Leaping
Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and
initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are
introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested
*
Corresponding author.
E-mail address: Kanagasabai Lenin (gklenin@gmail.com)
Journal of Applied Science, Engineering, Technology, and Education is licensed under an
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)Lenin | Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021), 114-118 114
in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power
loss considerably.
2. Problem Formulation
Objective of the problem is to reduce the true power loss:
(1)
Voltage deviation given as follows:
(2)
Voltage deviation given by:
(3)
Constraint (Equality)
(4)
Constraints (Inequality)
(5)
(6)
(7)
(8)
(9)
3.Enhanced Frog leaping algorithm
Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. F
frogs are engendered at initial to structure a group, in which frog i of the group is symbolized as
then individual frogs in the group are arranged in downhill order with reference to the fitness values, in order to
discover the global most excellent solution [17]. Groups are alienated into m ethnic groups, and each group
structured with n frogs, filling the relation F = m n. regulation of ethnic group division is formulated as : first frog
placed in the first sub-group, second frog placed in the second sub-group, frog m placed in the sub-group m, frog m +
1placed in the first sub-group again, frog m + 2 placed in the second sub-group; this alienation carried out, till all the
frogs are alienated. After that the most excellent frog in each sub-group, has to be found out and indicated by Pb; poor
frog indicated by Pw.
(10)
(11)
When the modernized is in the viable solution spaces then calculate the analogous fitness value of .
When the related fitness value of is poorer than the analogous fitness value of Pw, afterwards apply P w toLenin | Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021), 114-118 115
swap Pb in equation (10) and re-modernize ; after then also no development, then capriciously engender a
new-fangled frog to swap Pw; replicate the modernizing procedure until fulfilling end criterion
a. Produce capriciously the population of frogs
b. Fitness value of each frog has been calculated by its position.
c. Partition of the population will done into m memeplexes in the groups
d. For a definite number of evolutionary iterations each memeplex has to be developed. Subsequently
algorithm proceeds to the global exploration to shuffle memeplexes.
e. Categorize most excellent and poor frog.
f. Perk up the poor frog’s position; ; new-fangled position found by
. When this method creates an improved solution, then it swaps the poor frog. or
else, go to the subsequent step.
g. Reiterate the calculation in (10) and (11) when no improvements then go to subsequent step.
h. Engender a new-fangled random solution to swap the poor frog and repeat the procedure for a predefined
number of times.
i. After a definite number of memetic evolutionary steps within every memeplex Shuffle the memeplexes.
Then arrange the frogs in a downhill order with reference to the fitness value.
j. If the end criterion is reached then stop or else go to Step c.
Many times Frog leaping algorithm (FLA) falls into the local optimal solution particularly with respect to the problem
complexity. Mainly deliberation of the whole population positions around a local optimum and it cause an early
stagnation of the algorithm. In order to overcome this situation, an exploration acceleration factor has been induced as
follows,
(12)
Exploration acceleration factor “C” permit a greater change in frog’s position and thus, will broaden the global
exploration area at the commencement and focus the memetic evolution on a deeper local exploration in the region of
the optimal solution. In the proposed Enhanced Frog Leaping Algorithm (EFLA) alter the position of frogs in a
memeplex is done. As an alternative of moving only the poor frogs, most excellent frogs are also moved to execute a
local exploration and perk up their performance. Every most excellent frog has the identical scheme of altering its
position as poor frogs does in the Frog leaping algorithm.
i. Categorize the most excellent and the poor frog
ii. Previous to estimation of the poor frog’s position, perk up the most excellent frog is tried with the intention
of make sure of enhanced convergence of the memeplex which leads to superior solutions. Enhancement of
the most excellent frog’s position is attained by,
(13)
New-fangled position can be found by,
(14)
iii. Improvement the poor frog’s position is done through,
(15)
New-fangled position can be found by,
(16)Lenin | Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021), 114-118 116
iv. Reiterate the calculation in (15) and (16) with respect to the global most excellent frog. When there is no
enhancement then go to the subsequent step.
v. Engender a new-fangled capricious solution to swap the poor frog
Proposed Enhanced Frog Leaping Algorithm (EFLA) where the alteration in the ’memetic evolution’ has been done
which leads to improve the efficiency of the algorithm.
a. Produce capriciously the population of frogs
b. Fitness value of each frog has been calculated by its position.
c. Partition of the population will done into m memeplexes in the groups
d. For a definite number of evolutionary iterations each memeplex has to be developed. Subsequently
algorithm proceeds to the global exploration to shuffle memeplexes.
e. Categorize most excellent and poor frog.
Previous to estimation of the poor frog’s position, perk up the most excellent frog is tried with the intention
of make sure of enhanced convergence of the memeplex which leads to superior solutions. Enhancement of
the most excellent frog’s position is attained by;
New-fangled position can be found by; ; Improvement the poor frog’s position is done
through; ; New-fangled position can be found by; .
Reiterate the calculation with respect to the global most excellent frog. When there is no enhancement then
go to the subsequent step. Engender a new-fangled capricious solution to swap the poor frog.
f. After a definite number of memetic evolutionary steps within every memeplex Shuffle the memeplexes.
Then arrange the frogs in a downhill order with reference to the fitness value.
g. If the end criterion is reached then stop or else go to Step c.
4.Simulation Results
At first in standard IEEE 14 bus system the validity of the Projected Enhanced Frog Leaping Algorithm (EFLA) has
been tested & comparison results are presented in Table 1.
Table 1. comparison of loss
Control variables ABCO [19] IABCO [19] EFLA
V1 1.06 1.05 1.02
V2 1.03 1.05 1.01
V3 0.98 1.03 1.03
V6 1.05 1.05 1.00
V8 1.00 1.04 0.90
Q9 0.139 0.132 0.100
T56 0.979 0.960 0.900
T47 0.950 0.950 0.900
T49 1.014 1.007 1.000
Ploss (MW) 5.92892 5.50031 4.0192
Then IEEE 300 bus system [18] is used as test system to validate the performance of the Enhanced Frog Leaping
Algorithm (EFLA). Table 2 shows the comparison of real power loss obtained after optimization.Lenin | Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021), 114-118 117
Table 2 Comparison of Real Power Loss
Parameter Method EGA [21] Method EEA [21] Method CSA [20] EFLA
PLOSS (MW) 646.2998 650.6027 635.8942 617.0928
5.Conclusion
Enhanced Frog Leaping Algorithm (EFLA) successfully solved the optimal reactive power problem. In the proposed
Algorithm the most excellent frog information is used to augment the local search in each memeplex and initiate to
the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in
the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard
IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss
considerably.
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